Phase space integration method for bound states

1997 ◽  
Vol 65 (6) ◽  
pp. 563-564 ◽  
Author(s):  
Sharada Nagabhushana ◽  
B. A. Kagali ◽  
Sivramkrishna Vijay
Keyword(s):  
Phase Space ◽  
Bound States ◽  
Integration Method ◽  
Phase Space Integration

scholarly journals Accurate specific molecular state densities by phase space integration. II. Comparison with quantum calculations on H+3and HD+2

1992 ◽  
Vol 96 (9) ◽  
pp. 6842-6849 ◽  
Author(s):  
Michael Berblinger ◽  
Christoph Schlier ◽  
Jonathan Tennyson ◽  
Steven Miller
Keyword(s):  
Phase Space ◽  
Molecular State ◽  
Quantum Calculations ◽  
Phase Space Integration ◽  
State Densities

BOUND STATES FOR SCHRÖDINGER HAMILTONIANS: PHASE SPACE METHODS AND APPLICATIONS

1996 ◽  
Vol 08 (04) ◽  
pp. 503-547 ◽  
Author(s):  
PH. BLANCHARD ◽  
J. STUBBE
Keyword(s):  
Quantum Mechanics ◽  
Phase Space ◽  
Dynamical Systems ◽  
Bound States ◽  
Energy Levels ◽  
Schrödinger Operators ◽  
Particle Systems ◽  
Existence And Nonexistence ◽  
Phase Space Methods ◽  
Special Case

Properties of bound states for Schrödinger operators are reviewed. These include: bounds on the number of bound states and on the moments of the energy levels, existence and nonexistence of bound states, phase space bounds and semi-classical results, the special case of central potentials, and applications of these bounds in quantum mechanics of many particle systems and dynamical systems. For the phase space bounds relevant to these applications we improve the explicit constants.

scholarly journals THE QUAD SCHEME: A NEW METHOD FOR PHASE SPACE INTEGRATION.

1969 ◽  
Author(s):  
F.M. Mueller ◽  
J.W. Garland ◽  
M.H. Cohen ◽  
K.H. Bennemann
Keyword(s):  
Phase Space ◽  
New Method ◽  
Phase Space Integration

scholarly journals Neural network-based approach to phase space integration

2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Matthew Klimek ◽  
Maxim Perelstein
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Phase Space ◽  
Cross Section ◽  
Particle Physics ◽  
Probability Distributions ◽  
Excellent Performance ◽  
Phase Space Integration ◽  
Artificial Neural Network Ann

Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions on phase space. We present an Artificial Neural Network (ANN) algorithm optimized for this task, and apply it to several examples of relevance for particle physics, including situations with non-trivial features such as sharp resonances and soft/collinear enhancements. Excellent performance has been demonstrated, with the trained ANN achieving unweighting efficiencies between 30% – 75%. In contrast to traditional algorithms, the ANN-based approach does not require that the phase space coordinates be aligned with resonant or other features in the cross section.

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